Bair and Tibshirani [1]} combine supervised and unsupervised learning to predict survival times for cancer patients. They first employ a supervised approach to select a subset of genes from a gene expression dataset that correlates with survival. Then, unsupervised clustering is applied to these gene subsets to identify cancer subtypes. Once such subtypes are identified, they apply again supervised learning techniques to classify future patients into the appropriate subgroup or to predict their survival. Although the authors call the resulting approach semi-supervised, their setting is clearly different from ours.
